size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9956223
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] -0.76185566 -0.93340225 -0.84501723
[2,] 0.12708115 -0.03124988 -0.01349081
[3,] 1.24739759 1.13812864 1.17963561
[4,] -0.04205541 -0.28671793 -0.17495374
[5,] -0.07935895 0.06236680 0.10900799
[6,] -1.34155624 -1.40280715 -1.45978221
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.994 0.022 0.992 0.033 0.005 0.995
y3 0.994 1.000 0.053 0.995 0.062 0.033 0.991
x2 0.022 0.053 1.000 0.058 0.994 0.996 -0.007
y1 0.992 0.995 0.058 1.000 0.069 0.036 0.995
x1 0.033 0.062 0.994 0.069 1.000 0.993 0.001
x3 0.005 0.033 0.996 0.036 0.993 1.000 -0.026
y2 0.995 0.991 -0.007 0.995 0.001 -0.026 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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